254 related articles for article (PubMed ID: 32101147)
1. The application of unsupervised deep learning in predictive models using electronic health records.
Wang L; Tong L; Davis D; Arnold T; Esposito T
BMC Med Res Methodol; 2020 Feb; 20(1):37. PubMed ID: 32101147
[TBL] [Abstract][Full Text] [Related]
2. Comparison of Machine Learning Methods With Traditional Models for Use of Administrative Claims With Electronic Medical Records to Predict Heart Failure Outcomes.
Desai RJ; Wang SV; Vaduganathan M; Evers T; Schneeweiss S
JAMA Netw Open; 2020 Jan; 3(1):e1918962. PubMed ID: 31922560
[TBL] [Abstract][Full Text] [Related]
3. Automated feature selection of predictors in electronic medical records data.
Gronsbell J; Minnier J; Yu S; Liao K; Cai T
Biometrics; 2019 Mar; 75(1):268-277. PubMed ID: 30353541
[TBL] [Abstract][Full Text] [Related]
4. Building interpretable predictive models for pediatric hospital readmission using Tree-Lasso logistic regression.
Jovanovic M; Radovanovic S; Vukicevic M; Van Poucke S; Delibasic B
Artif Intell Med; 2016 Sep; 72():12-21. PubMed ID: 27664505
[TBL] [Abstract][Full Text] [Related]
5. Prediction of 30-Day All-Cause Readmissions in Patients Hospitalized for Heart Failure: Comparison of Machine Learning and Other Statistical Approaches.
Frizzell JD; Liang L; Schulte PJ; Yancy CW; Heidenreich PA; Hernandez AF; Bhatt DL; Fonarow GC; Laskey WK
JAMA Cardiol; 2017 Feb; 2(2):204-209. PubMed ID: 27784047
[TBL] [Abstract][Full Text] [Related]
6. CPAE: Contrastive predictive autoencoder for unsupervised pre-training in health status prediction.
Zhu S; Zheng W; Pang H
Comput Methods Programs Biomed; 2023 Jun; 234():107484. PubMed ID: 37030137
[TBL] [Abstract][Full Text] [Related]
7. Predicting post-stroke pneumonia using deep neural network approaches.
Ge Y; Wang Q; Wang L; Wu H; Peng C; Wang J; Xu Y; Xiong G; Zhang Y; Yi Y
Int J Med Inform; 2019 Dec; 132():103986. PubMed ID: 31629312
[TBL] [Abstract][Full Text] [Related]
8. Applying interpretable deep learning models to identify chronic cough patients using EHR data.
Luo X; Gandhi P; Zhang Z; Shao W; Han Z; Chandrasekaran V; Turzhitsky V; Bali V; Roberts AR; Metzger M; Baker J; La Rosa C; Weaver J; Dexter P; Huang K
Comput Methods Programs Biomed; 2021 Oct; 210():106395. PubMed ID: 34525412
[TBL] [Abstract][Full Text] [Related]
9. Electronic phenotyping of health outcomes of interest using a linked claims-electronic health record database: Findings from a machine learning pilot project.
Gibson TB; Nguyen MD; Burrell T; Yoon F; Wong J; Dharmarajan S; Ouellet-Hellstrom R; Hua W; Ma Y; Baro E; Bloemers S; Pack C; Kennedy A; Toh S; Ball R
J Am Med Inform Assoc; 2021 Jul; 28(7):1507-1517. PubMed ID: 33712852
[TBL] [Abstract][Full Text] [Related]
10. Predicting hospitalizations from electronic health record data.
Morawski K; Dvorkis Y; Monsen CB
Am J Manag Care; 2020 Jan; 26(1):e7-e13. PubMed ID: 31951361
[TBL] [Abstract][Full Text] [Related]
11. Machine Learning Model for Risk Prediction of Community-Acquired Acute Kidney Injury Hospitalization From Electronic Health Records: Development and Validation Study.
Hsu CN; Liu CL; Tain YL; Kuo CY; Lin YC
J Med Internet Res; 2020 Aug; 22(8):e16903. PubMed ID: 32749223
[TBL] [Abstract][Full Text] [Related]
12. Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.
Liang Z; Liu J; Ou A; Zhang H; Li Z; Huang JX
Comput Methods Programs Biomed; 2019 Jun; 174():17-23. PubMed ID: 29801696
[TBL] [Abstract][Full Text] [Related]
13. Deep Learning-based Propensity Scores for Confounding Control in Comparative Effectiveness Research: A Large-scale, Real-world Data Study.
Weberpals J; Becker T; Davies J; Schmich F; Rüttinger D; Theis FJ; Bauer-Mehren A
Epidemiology; 2021 May; 32(3):378-388. PubMed ID: 33591049
[TBL] [Abstract][Full Text] [Related]
14. A machine learning-based framework to identify type 2 diabetes through electronic health records.
Zheng T; Xie W; Xu L; He X; Zhang Y; You M; Yang G; Chen Y
Int J Med Inform; 2017 Jan; 97():120-127. PubMed ID: 27919371
[TBL] [Abstract][Full Text] [Related]
15. Unsupervised anomaly detection of implausible electronic health records: a real-world evaluation in cancer registries.
Röchner P; Rothlauf F
BMC Med Res Methodol; 2023 May; 23(1):125. PubMed ID: 37226114
[TBL] [Abstract][Full Text] [Related]
16. Evaluation of an Algorithm for Identifying Ocular Conditions in Electronic Health Record Data.
Stein JD; Rahman M; Andrews C; Ehrlich JR; Kamat S; Shah M; Boese EA; Woodward MA; Cowall J; Trager EH; Narayanaswamy P; Hanauer DA
JAMA Ophthalmol; 2019 May; 137(5):491-497. PubMed ID: 30789656
[TBL] [Abstract][Full Text] [Related]
17. Treatment effect prediction with adversarial deep learning using electronic health records.
Chu J; Dong W; Wang J; He K; Huang Z
BMC Med Inform Decis Mak; 2020 Dec; 20(Suppl 4):139. PubMed ID: 33317502
[TBL] [Abstract][Full Text] [Related]
18. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set.
Rasmy L; Wu Y; Wang N; Geng X; Zheng WJ; Wang F; Wu H; Xu H; Zhi D
J Biomed Inform; 2018 Aug; 84():11-16. PubMed ID: 29908902
[TBL] [Abstract][Full Text] [Related]
19. Representation learning for clinical time series prediction tasks in electronic health records.
Ruan T; Lei L; Zhou Y; Zhai J; Zhang L; He P; Gao J
BMC Med Inform Decis Mak; 2019 Dec; 19(Suppl 8):259. PubMed ID: 31842854
[TBL] [Abstract][Full Text] [Related]
20. Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.
Si Y; Du J; Li Z; Jiang X; Miller T; Wang F; Jim Zheng W; Roberts K
J Biomed Inform; 2021 Mar; 115():103671. PubMed ID: 33387683
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
